---
license: mit
tags:
  - robotics
  - libero
  - diffusion-policy
  - patch-policy
  - dinov2
datasets:
  - ParsaSharifi/patch-policy-libero-spatial
---

# Patch Policy diffusion on LIBERO Spatial

A [Patch Policy](https://patch-policy.github.io/) diffusion head trained on **LIBERO Spatial**: frozen DINOv2 ViT-S/14
patch tokens from both cameras (512 tokens), conditioned on a per-task goal image, predicting 7-d end-effector
actions. Checkpoint of epoch 122, the best of a 200-epoch run after a 500-episode re-evaluation.

## Result

| checkpoint | success | episodes |
|---|---|---|
| epoch 122 (this checkpoint) | **0.944** | 500 (50 per task) |
| epoch 122 | 0.99 | 100 (10 per task) |

Per task, epoch 122, 500 episodes:

| task: pick up the black bowl ... | success |
|---|---|
| between the plate and the ramekin | 0.88 |
| from table center | 1.00 |
| in the top drawer of the wooden cabinet | 0.98 |
| next to the cookie box | 1.00 |
| next to the plate | 0.80 |
| next to the ramekin | 0.98 |
| on the cookie box | 0.96 |
| on the ramekin | 0.96 |
| on the stove | 0.98 |
| on the wooden cabinet | 0.90 |

- Evaluation protocol of the patch-policy code, not the LIBERO standard: a fresh seeded reset per episode
  (seed 42 + k, objects jittered within the task's regions), 300-step limit. The LIBERO standard uses 50 fixed initial
  states per task and 220 steps, so this number is not directly comparable to published LIBERO Spatial results.
- All sweep and confirmation results are in `results/` (`summary.json`, `eval/model_<epoch>.json`,
  `eval/confirm/`). The 10 goal images are in `results/goal_images/`.

## Training

| setting | value |
|---|---|
| code | patch_policy_cleanup, branch `feat/libero-spatial` (commit `7fda225`), config `libero_spatial/diffusion` |
| data | [ParsaSharifi/patch-policy-libero-spatial](https://huggingface.co/datasets/ParsaSharifi/patch-policy-libero-spatial): the 500 official LIBERO Spatial demos re-rendered at 224x224 |
| observation / action windows | 2 / 3 (prediction horizon 4) |
| batch, epochs, optimiser | 255 (85 x 3 GPUs), 200, AdamW lr 5e-5, weight decay 2e-4 |
| denoiser | transformer 8 layers x 4 heads x 256; DDPM 100 steps, EMA weights used for evaluation |
| precision | bf16 autocast, torch.compile, TF32 (DINOv2 runs in fp32) |
| goal | last frame of each task's first-listed demo (`demo_36`), both cameras, concatenated per token |
| hardware, time | 3 x RTX 3090, 4 h |

## Files

```
outputs/libero_spatial/diffusion/dino_patch-2026-09-21_212317/checkpoints/model_0122.pt   # epoch, config, policy_state_dict
results/                                                                                 # evaluation results, goal images, frame strips
```

The checkpoint is a PyTorch pickle (`torch.load(..., weights_only=False)`): load it only if you trust this repo.
DINOv2 is not included; the code loads it from `torch.hub` (`facebookresearch/dinov2`).

## Evaluate

From a checkout of the code on branch `feat/libero-spatial`, with the dataset downloaded:

```bash
hf download ParsaSharifi/patch-policy-libero-spatial-diffusion --local-dir ~/pp_libero_spatial
MUJOCO_GL=egl uv run python evaluate.py \
  ~/pp_libero_spatial/outputs/libero_spatial/diffusion/dino_patch-2026-09-21_212317 --epochs 122 --no-wandb
```

- The config stored in the checkpoint names the dataset directory it was trained with
  (`dataset.data_directory: /media/sda1/parsa/original_patch_policy/patch_policy_datasets/libero_dataset`). The evaluation
  loads the goal images from there, so the dataset must be at that path (or a symlink to it).
- The goal image of each task is the last frame of the first demo its folder lists. On the training machine that is
  `demo_36` for every task; directory listing order depends on the filesystem, so check it on a new machine.